THE AUTONOMOUS MINI HELICOPTER:
A POWERFUL PLATFORM FOR MOBILE MAPPING
Henri Eisenbeiss
Institute of Geodesy and Photogrammetry, ETH Zurich, CH-8093, Zurich, Switzerland, +41 44 633 32 87
ehenri@geod.baug.ethz.ch
Commission Ⅰ, ICWG I/V
KEY WORDS:
Acquisition, Automation, Integration, Mobile, Mapping, Processing, UAV
ABSTRACT:
In this paper, the developments related to an autonomous airborne mobile mapping system for photogrammetric processing will be
presented. During the last years the author has been involved in several projects related to mobile mapping using an autonomously
flying model helicopter, a so-called mini UAV (Unmanned Aerial Vehicle). The overall motivation of using mini UAVs for mobile
mapping, the developed workflow for UAV-data processing, the current status of the work, and recent developments related to
specific applications are described. In a first step our mini UAV-system and our approach are explained and the need for specific
developments is highlighted. In the following two applications will be described: The Randa rockslide and the maize project,
demonstrating our developments for the automation of the photogrammetric workflow using the mini UAV system. In the Randa
project a unique flight planning tool for UAV-monitoring of mountainous areas was developed. The tool allows for the adaptation of
the flight in a way, that image data with 2-3 cm resolution of the cliff could be acquired. Moreover, due to this high image resolution,
a DSM with a higher point density compared to standard airborne and helicopter-based LIDAR-systems, could be generated. In the
second project the focus was on the automation of the image orientation process. Hence, two test areas (A and B) were defined.
While in A two commercial photogrammetric workstation were evaluated, in B the data set was processed using GPS/INS data as
initial values for the image orientation. Therefore, the manual input for the aerial triangulation was reduced to a minimum user
interaction. Finally, with the oriented image data two DSMs for the analysis of the outcrossing in maize were generated. Because of
these developments, the tools for the planning and the data acquisition as well as the workflow for the processing of UAV-images
were automated. However, the complete workflow still requires manual interaction, which will be discussed here in detail including
a proposal for future developments.
1.
(Bendea et al., 2007) and, in contrast to Microdrones (Nebiker et
al., 2007), more stable against environmental conditions like wind.
The developments of model helicopters and comparable
autonomous vehicles are primarily driven by the artificial
intelligence community (AAAI, 2008) and have been used mainly
in the past for military applications with increasing use in the
civilian sector.
INTRODUCTION
Throughout the last years, UAVs (Unmanned Aerial Vehicles)
have become more and more suitable platforms for data
acquisition in photogrammetry and mobile mapping. “UAVs
are to be understood as uninhabited and reusable motorized
aerial vehicles.” (van Blyenburg, 1999). These vehicles are
remotely controlled, semi-autonomous, autonomous, or have a
combination of these capabilities. The term UAV is used
commonly in the computer science, robotics and artificial
intelligence communities. Supplementary, in the literature also
synonyms like Remotely Piloted Vehicle (RPV), Remotely
Operated Aircraft (ROA) and Unmanned Vehicle Systems
(UVS) can be found. The definition of UAVs encompasses
fixed and rotary wings UAVs, lighter-than-air UAVs, lethal
aerial vehicles, aerial decoys, aerial targets, alternatively
piloted aircrafts and uninhabited combat aerial vehicles.
Sometimes, cruise missiles are also referred to as UAVs (van
Blyenburg, 1999).
In the past, model helicopters were already used in
photogrammetric applications (Eisenbeiss, 2004). However at that
time, the model helicopters were controlled manually via radio
link. Nowadays, these new technologies allow low cost
navigation systems to be integrated in model helicopters, enabling
them to fly autonomously. This kind of autonomous flying model
helicopter is called mini UAV system (Blyenburg, 1999;
Eisenbeiss 2004). These mini UAVs are highly manoeuvrable due
to the possibility of hovering, change of flight direction around
the center of rotation as well as the capability for turning the
mounted camera in horizontal and vertical direction. However,
due to the difficulty of keeping the ideal position and attitude, the
vibration of the helicopter and the manual planning of image
acquisition points, model helicopters have not been used
successfully in the past for measurements, precise modeling and
mapping of objects (Eisenbeiss, 2004). Latest developments
integrate GPS/INS (Global Positioning System / Inertial
Navigation System) together with a stabilization platform for the
camera. Because of the small size and the low payload, the
selection of the installed hardware is mostly limited to low cost
navigation systems with low precision. Nevertheless, the
However, a model helicopter was selected for our
investigations. Model helicopters are clearly defined by the
Unmanned Vehicle Systems (UVS) International Association as
mini, close short and medium range UAVs depending on their
size, endurance, range and flying altitude (UVS, 2008).
In contrast to standard airplanes, model helicopters are able to
operate closer to the object. In addition, these systems are
highly flexible in navigation compared to fixed wing UAVs
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parameters. Additionally, the interfaces have the functionality for
data transformation and formatting. Therefore, with our proposed
workflow several UAV applications can be handled. The main
modules are defined as follows: Project parameters (PP), Flight
planning (FP), Autonomous photogrammetric flight (APF),
Quality check of the data (QCD), UAV Block Triangulation
(UAV BT), DSM, orthophoto, 3D Model (DO3D). The individual
procedures of a module can be a process or a part of a process
from commercial or in-house developed software.
Figure Figure 1 show which module are or will be developed by
IGP and which focus more on the evaluation of existing
commercial or in-house developed software packages.
Additionally, figure 1 explains which part of the work will be
possible during or after the field work or both.
combination of GPS/INS sensors with image data for
navigation allows more precise and reliable results.
Furthermore, the integration of GPS/INS and image data in a
real time triangulation method will drastically reduce time and
cost required for post processing.
Mini UAVs have been used recently for civilian applications
like 3D city modeling (Wang et al., 2004), modeling of a
medieval castle in Sarnen (Pueschel et al., 2008), Horcher and
Visser (2004) proposed applications in forestry like BMP
Inspections, road maintenance of forest roads and trespas.
Further on, UAVs were used in applications related to
agriculture (Herwitz et al., 2004; Rovira-Más et al., 2005;
Eisenbeiss, 2007; Reidelstuerz et al., 2007; Rovira-Más et al.,
2008) and for the documentation of an archaeological site
(Lambers et al., 2007). In these projects the focus was on a fast
data processing, which reduces the accuracy of the results or in
contradiction the results had high accuracy, while the
processing cost still a lot of manual effort.
In the following, two developments related to real applications of
such a mini UAV-system, which lead to the automation of the
workflow, will be presented. Before describing the developments
in detail, our mini UAV-system and its ground control station will
be described. After all, the outcome of the developments and the
direct benefits using an UVS system in the two specific
applications are discussed. Finally, we will propose a new UAVplatform, which can lift a bigger payload.
Hence, our goal is the automation of the complete workflow in
a way that the manual input can be reduced to an acceptable
minimum. A further objective of our work is the high reliability,
precision and resolution of the photogrammetric products like
elevation models, orthophoto and textured 3D models.
Therefore, the aim is to develop procedures for the UAV data
acquisition and processing that can lead to a highly automated
workflow and accurate complete results (see Figure 1).
2. AUTONOMOUS MINI HELICOPTER
The mini UAV-system Copter 1b (see Figure 4 and Table 1) was
developed by the French company Survey-Copter. It is equipped
with an on-board navigation system (wePilot 1000) from
weControl. The wePilot includes a GPS (Global Positioning
System), an INS (Inertial Navigation System), which allows the
mini UAV to perform attitude stabilization, position control and
to navigate autonomously. The private company weControl
GmbH is a spin-off of ETH Zurich that specialized in the
development of miniature flight control systems for UAV-systems.
Mini UAV-system Copter 1b
Length
2m
Rotor diameter
1.8m
Maximum takeoff weight
15kg
Payload capacity
5kg
Flight endurance
Max. 45min
Altitude
1500m
Range
5km
Table 1: Specifications of the mini UAV Copter 1b
(SurveyCopter, 2008).
The navigation system features the following main characteristics:
an altitude stabilization and velocity control, position and RC
transmitter sticks interpreted as velocity commands, integrated
GPS/INS system, altimeter, magnetometer, payload intensive
flight controller, built-in data logger and telemetry capability,
programmable hardware for rapid customization and an embedded
computer system. Furthermore, the system consists of a ground
control station (a laptop with monitoring software (weGCS)), a
convertible gimbal for still video cameras like Canon EOS D10,
D20 and the Nikon D2Xs, communication links, power supply,
video link (incl. video camera) for visual control for monitoring
image overlap, and transport equipment.
Figure 1: Modular Workflow for processing of UAV data
showing the main functionalities.
All processing steps of the workflow are categorized in
modules, which communicate with each other via interfaces.
The interfaces are established in a way that for the individual
module the attributes can change, depending on the project
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The ground control station also includes a flight simulator,
which allows the simulation of the real flight to verify the
predefined flight path (see Figure 2).
3.
3.1
Therefore, a high-resolution digital surface model (DSM) with a
spatial resolution of 10–20cm had to be generated and oriented
images with a footprint of 2-5cm in object space had to be
acquired. Because of the non-nadir case and a difficult terrain, the
standard aerial flight planning procedures could not be applied.
RANDA
Furthermore, the sight was acquired by a manned helicopter in
November 2007, using the Helimap system, combining laser
scanning and oblique images (Vallet, 2007). The system is based
on a Riegl LMS-Q240i-60 laser scanner for point cloud
generation and a Hasselblad H1 camera with a Dos Imacon
Xpress 22Mpix back, which is normally used for texture mapping.
Project Aims
The focus of the geological project Randa is to characterize the
large-scale rockslide in Randa (Wallis, Swiss Alps; see Figure
3). This rockslide will be used as an example of the ultimate
evolutionary stages corresponding to the complete development
of the basal shear surface(s) and internal rock mass deformation.
In order to understand this failure stage in a better way, the
interrelationships between pre-existing tectonic fractures,
subsequent tensile fracturing and shearing of intact rock bridges
will be investigated. Furthermore, for the understanding of the
rock mass structure and kinematics during this evolutionary
stage, the basal shear planes must be identified, the internal
rock mass deformation has to be further studied, and the 3D
time-dependent displacement field has to be measured more in
detail using the UAV-image data and the generated DSM
(Randa, 2008).
Using the two comparable systems, the autonomous flying model
helicopter and the manned helicopter, at one site, allowed the
analysis of the performance of the systems. Moreover, it is
possible to compare and integrate the two data sets.
3.2 Flight planning
Before doing the flight, the main parameters of the autonomous
flight were defined. For the Randa rockslide we decided to use the
Nikon D2Xs, which has a CMOS-sensor with 4288x2848 pixels
using a 50mm lens (Nikon, AF NIKKOR 50mm 1:1.8D) and a
pixel size of 5.5µm. For the recognition of features with a length
of 10-20cm, an image scale of 1:4500 was selected which results
in a pixel footprint of approximately 3cm. The distance to the cliff
was defined to 230m, which is equal to the normally used flying
height above ground. Finally, the side and end lap were set to
75%. Using this high overlapping along and across strip it was
possible to avoid occlusions and gaps in the image data (see
Figure 6). The flight velocity was defined to 3m/s, while the
shutter speed was 1/1000s. Therefore, the influence of the image
motion was negligible.
After defining these parameters, the most recent elevation model
with the highest available point density was used. Therefore, the
LiDAR data provided by swisstopo was selected. Using this data
set and the available orthophoto (swissimage, swisstopo®)
allowed the definition of the area of interest. Hence, the area was
separated in three sections. The sections were selected using the
inclination and aspect values of the slope.
Figure 2: Screenshot of the ground control station software
showing the flight lines of the Randa data acquisition. In offline mode the software can be used for the simulation of the
flight. The orange shading shows the lower part of the Randa
rock cliff.
Figure 4: Our model helicopter during a test flight with the
inclined camera looking perpendicular to the flight direction.
For each particular section a plane was defined in a way that the
average 3D distance of the surface to the plane was reduced to a
minimum. While looking perpendicular to the surface, we
assumed to reduce the occlusion in the images. After the
definition of the plane equation, the normal of the plane was
Figure 3: The Randa rockslide failure (Wallis, Switzerland).
The lower part is situated 1400 a.s.l. The height difference from
the bottom to the top is more than 800m.
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calculated and with the given flying distance to the surface a
parallel plane was defined (see Figure 5). In the new plane the
area of interest was projected and the inclined flight lines for
the helicopter flight were generated. Finally, using this
information the image acquisition points were determined and
transferred to the navigation unit (wePilot1000) of the mini
UAV-system. Due to the inclination of the plane the camera
was mounted with a pitch angle of 15-30° (respectively for the
individual sections) on the helicopter (see Figure 4).
3.3
Field Work
Since the Randa rockslide covers a height from 1400m -2300m
a.s.l. the mini UAV-system was tested at the Flueelapass (~2400m
a.s.l., Graubuenden, Switzerland). For the flight at Flueelapass
weight dummies instead of the mounted camera were used on the
helicopter. To enable the evaluation of the performance at such a
height above mean sea level, the helicopter was started and
switched to the assisted flying mode. In this mode the current
parameters of the engine were checked visually and saved on
board of the helicopter. This specific test showed that the engine
of our mini UAV-system already achieved the limit for the
maximal turning moment of the engine. Therefore, to have a
buffer, we decided to do the first flight only at the lower part of
the rockslide (1400-1800m a.s.l., see Figure 3), while the upper
part was also covered by the Helimap flight.
Such precise planning was necessary due to the large distance
between operator and mini UAV, which limited the visibility of
the mini UAV. Furthermore, before flying along the predefined
flight path, the mini UAV was tested at an altitude of 2400m
a.s.l. in order to ensure that the engine of the helicopter would
not fail due to low air pressure. This test was important, since
the helicopter manufacturer set the maximum flying altitude to
1500m a.s.l. In the following, the results from the first flight in
Randa and an elevation model extracted from the acquired data
will be described.
Since the illumination conditions were acceptable only in the
morning, we decided to do the flight at Randa in the morning
hours. In the afternoon strong shadows made the data processing
more complicated, while a preprocessing of shadow areas had to
be accomplished. Furthermore, the GPS-satellite availability was
simulated in the preparation process. Thus, the existing elevation
model of the surrounding (DHM25, swisstopo®) was integrated
in a GPS sky plot software, which allowed the calculation of the
accessible number of GPS and the postulated GDOP (Geometric
Dilution Of Precision) value. Using this information, it was
possible to have an approximate a-posteriori value for the
accuracy of the GPS-position of the helicopter, which was crucial
in such an environment.
Given that at the bottom the site a large debris zone is situated
(see Figure 3), the accessibility of the site is quite complicated.
Therefore, the start and landing point was defined close to a road
at the bottom of the debris zone (see Figure 2). The field work
itself was done in few minutes, while the longest part of the flight
was the way to go from the starting point to the first image
acquisition point and the way back. At the end of the first flight,
the helicopter did a fast in-explainable turn along its own main
axis. Hence, we decided to stop the test and to evaluate the flight
data, having already the first high resolution images from the
rockslide.
Figure 5: Zoom-in to one section of the cliff showing the
LiDAR-DSM with the approximated and parallel plane from
the flight planning.
Figure 6: Left: Derived surface model from image matching, Middle: Zoom-in of an UAV-image, Right: Point cloud of Helicopterbased LiDAR projected on the derived surface model.
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3.4 Results
4.2 Field work
Using the acquired UAV-images, a DSM of the lower part of
the Randa rockslide with 15cm resolution was generated (see
Figure 6). The complete site was documented with the Helimap
system. For the Helimap system, the pixel size was 6cm to 8cm.
In comparison to the flight defined for the mini UAV-system,
the Helimap flight was controlled by a pilot, who followed the
flight lines with an accuracy of 30m. Therefore, for the whole
area the scale varies depending on the complexity of the terrain.
Furthermore, the Helimap system acquired the cliff with an
oblique field of view, which evokes the gaps through occlusion
in the data set. For the laser scan, the final point density was
approximately 3pt/m2 (see Figure 6).
After anthesis, digital pictures were captured with a still-video
camera (Canon D20) mounted on our mini UAV-system
(Eisenbeiss, 2007).
In the first step, a flight planning was performed for the
autonomous flight. The image resolution was defined to have 3
cm per pixel in object space. Furthermore, the maximum flying
height was set to 100m above ground, due to the nearness to
Zurich airport. With the selected camera the remaining parameters
were calculated (see Table 2).
After definition of the flight parameters, the area of interest was
roughly identified in the Swiss National Map 1: 25 000 provided
by swisstopo. With the defined flight parameters, a trajectory was
calculated by starting at one corner of the field and 3Dcoordinates of the flight trajectory were established. The
autonomous flight was done using our UAV system. Following
the flight trajectory in the stop mode for experiment A, the
camera captured the images automatically on the predefined
positions in the cruising flight mode for experiment B. In the stop
mode the helicopter flew autonomously to each image acquisition
point and the operator triggered the image via radio link, while
during the cruising mode the image acquisition was completely
autonomous.
The visual comparison of the extracted UAV-DSM and the
LiDAR-DSM shows clearly that the fine structure of the cliff
could be modeled out of the UAV-DSM, while the LiDARDSM shows big holes and less resolution (see Figure 6).
The analysis of the unexpected turning of the UAV-system
during the flight showed that the effect was caused by the fact
that the neighboring points of the flight plan were defined to
close to each other. Furthermore, the effect evoked while the
neighboring flight lines had a distance in horizontal and vertical
direction, which caused a miss-interpretation in the flight
control software. Hence, during the flight, one predefined data
acquisition point was skipped and the turning occurred during
the flight at Randa. Therefore, the flight control software was
improved to handle autonomous flights flown with this type of
configuration.
4.3
Due to the autonomous flight with stop points we could capture
all images for experiment A in 20 minutes. By using the cruising
flight modus we reduced the flight time for experiment B to 5
minutes.
4. MAIZE FIELD
4.1
Project Aims
For the image triangulation for experiment A 2-3 tie points were
measured manually, while for the experiment B the orientation
values calculated using the Kalman filter implemented in the
navigation unit on board the mini UAV were used. For 5-10
percent of the images the 2-3 manually measured points were not
enough, since during the image acquisition the light breeze moved
the leaves of the plants slightly. Therefore, the measurement of
more manual points was essential (Eisenbeiss, 2007). The
manually measured points (experiment A) and the approximate
positions of the camera (experiment B) served as initial values for
automated tie point extraction. For the generation of the tie points,
the standard procedure implemented in LPS (Leica
Photogrammetry Suite) was used. Therefore, the initial
approximations of each image location within the projects had to
be defined. Hence, for area B the function for exterior orientation
parameter input and for area A the function for manual tie point
measurement were selected. After the generation of tie points, the
control points were manually measured in the images.
The goal of this study was to analyze the influence of terrain
characteristics on pollen dispersal in maize. For that application,
the task was to generate 3D elevation models as well as to use
the oriented images for a stereoscopic inspection of maize
fields, and to combine them with high resolution crosspollination data. The elevation model and the stereoscopic
measures were used to determine the heights of pollen donor
and receptor plants with respect to their absolute orthometric
height. For these investigations, two test areas (2005: A and
2006: B) were defined.
Parameter
Value
Image scale
1:4000
Side / end lap
75% / 75 %
Flying height above ground
~ 80 m
Camera
Canon EOS 20D
Focal length (calibrated)
Pixel (Image format)
20.665, RMSE 1.5e-003 mm
8.25 megapixels (3520x2344)
Flying velocity
3 m/s
Data Processing
Since the maize fields had a highly repetitive structure in the
image, it was necessary to detect blunders before doing image
orientation. In LPS, a blunder can be detected only by manual
checking until the project has proceeded to the point where a
bundle adjustment is possible. Therefore the blunder detection
with ORIMA (Leica Geosystems) integrated in LPS and ISDM
(Image Station Digital Mensuration, Intergraph) was attempted.
These software packages allow a relative orientation for each
stereo pair, multi-image blocks and the detection of blunders.
Finally, with both software packages the blunders were detected
and the orientation of the UAV-images was performed with an
Table 2: Flight parameters for the image observation of the
maize field in 2005 and 2006
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comparable fixed wing systems due to the system characteristics.
Therefore, the used autonomous flying helicopter system can be
introduced in several applications like crop maps for biomass
measurement, as input for tractor-automated guidance (Kise et al.,
2005), in forestry, for monitoring and detection and prediction of
erosion using the extracted image data, orthophotos and elevation
model. Furthermore, the mini UAV-system can be used for 3D
city modeling in combination with an autonomous car (Lamon et
al., 2006).
sigma 0 a posteriori of approx. 0.5 pixel and the RMS error for
the control points was below 1dm for both test areas.
4.4 Results
After image orientation, a DSM with a resolution of 10 cm was
produced automatically with the in-house developed software
package SAT-PP (Satellite imagery Precise Processing). The
software was initially developed for the processing of satellite
images and later adapted such that it was capable to handle
still-video camera and aerial images. Due to the combination of
the multi image approach and the combination of different
matching methods (area, feature and edge matching) (Zhang,
2005) allowed the generation of a precise, reliable and
consistent 3D model of the maize field. Finally, an orthophoto
with a footprint of 3 cm was produced using the generated
DSM and integrated into a GIS for further analysis (see
Figure 7).
In comparison to the mobile stereovision system, proposed by
Rovira-Mas et al. (2005) and Rovira-Mas et al. (2008), our
method allows an autonomous documentation with the UAV
system by producing accurate and reliable elevation models and
orthoimages of the site. The produced data can be directly
integrated into several GIS systems using absolute coordinates.
Therefore, the data can be easily combined with any other
reference data. Furthermore, by using medium format still-video
cameras, the image and the derived DSM resolution allows
analysis at a larger scale as with stereovision systems using the
same flight height. Therefore, large sites can be documented
completely in a shorter time period with our method.
Orientation values were calculated by using the Kalman filter
implemented in the navigation unit on board the mini UAV as
initial exterior orientation parameters for the photogrammetric
processing for experiment B; we could reduce the manual effort
during the photogrammetric processing. However, the complete
workflow still requires manual measurements for the control
points and statistical analysis of the results. This lack of
automation has to be the emphasis for future work by
development of online-triangulation methods and the automated
measurements of control points using coded targets.
In future work, we will also focus on the combination of LiDAR
and image data. Since our model helicopter has a maximum
payload of 5kg, a more powerful UAV-system (Aeroscout Scout
B2-120; Aeroscout) will be used, which also features the flight
control system of weControl. The combination of LiDAR and
image data allows us to use the strength of both systems: the laser
scanner is good in areas with less texture, while image data is
advantageous for edge measurement and texture mapping.
Figure 7: Screenshot of the 3D-model of experiment B (2006)
using the Swissimage orthoimage (swisstopo®) draped over the
DHM25 (swisstopo®) combined with the orthoimage of the
maize field and the position of the cross-pollination data.
5.
CONCLUSIONS AND FUTURE WORK
ACKNOWLEDGEMENTS
In this study, a workflow for the processing of UAV data was
presented. The processing steps of the workflow are based on
modules, which allow the independent adaptation of the
individual steps.
The author thanks Kirsten Wolff and Martin Sauerbier for their
contribution to the paper and Manuel Kaufmann for his input to
the Randa project.
The autonomous flight allowed us to predefine the flight
trajectory for an optimal photogrammetric processing. The
flight trajectories for both projects were calculated
automatically by in-house developed software using existing
topographic maps, orthophotos and elevation models. For the
Randa project, the flight planning was modified to the nonnadir case. Two autonomous flying modes were applied in the
maize field project: stop modus and cruising modus. By using
the cruising mode the flight time could be reduced by 75
percent for the observation of one field. The performance of the
system during the cruising mode is comparable with fixed-wing
UAV systems (Horcher and Visser, 2004), while the stabilizer
system of the helicopter allowed a continuous and stabilized
flight. In addition, the helicopter system is more flexible than
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